Date of Award

Fall 12-15-2022

Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Hever Moncayo

First Committee Member

Richard Prazenica

Second Committee Member

Maj Mirmirani

College Dean

James W. Gregory

Abstract

The use of autonomous flight vehicles has recently increased due to their versatility and capability of carrying out different type of missions in a wide range of flight conditions. Adequate commanded trajectory generation and modification, as well as high-performance trajectory tracking control laws have been an essential focus of researchers given that integration into the National Air Space (NAS) is becoming a primary need. However, the operational safety of these systems can be easily affected if abnormal flight conditions are present, thereby compromising the nominal bounds of design of the system's flight envelop and trajectory following. This thesis focuses on investigating methodologies for modeling, prediction, and protection of autonomous vehicle trajectories under normal and abnormal flight conditions. An Artificial Immune System (AIS) framework is implemented for fault detection and identification in combination with the multi-goal Rapidly-Exploring Random Tree (RRT*) path planning algorithm to generate safe trajectories based on a reduced flight envelope. A high-fidelity model of a fixed-wing unmanned aerial vehicle is used to demonstrate the capabilities of the approach by timely generating safe trajectories as an alternative to original paths, while integrating 3D occupancy maps to simulate obstacle avoidance within an urban environment.

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